A clinically validated prediction method for facial soft-tissue changes following double-jaw surgery

The Accuracy of Three-Dimensional Soft Tissue Simulation in Orthognathic Surgery-A Systematic Review

Three-dimensional soft tissue simulation has become a popular tool in the process of virtual orthognathic surgery planning and patient-surgeon communication. To apply 3D soft tissue simulation software in routine clinical practice, both qualitative and quantitative validation of its accuracy are required. The objective of this study was to systematically review the literature on the accuracy of 3D soft tissue simulation in orthognathic surgery. The Web of Science, PubMed, Cochrane, and Embase databases were consulted for the literature search. The systematic review (SR) was conducted according to the PRISMA statement, and 40 articles fulfilled the inclusion and exclusion criteria. The Quadas-2 tool was used for the risk of bias assessment for selected studies. A mean error varying from 0.27 mm to 2.9 mm for 3D soft tissue simulations for the whole face was reported. In the studies evaluating 3D soft tissue simulation accuracy after a Le Fort I osteotomy only, the upper lip and paranasal regions were reported to have the largest error, while after an isolated bilateral sagittal split osteotomy, the largest error was reported for the lower lip and chin regions. In the studies evaluating simulation after bimaxillary osteotomy with or without genioplasty, the highest inaccuracy was reported at the level of the lips, predominantly the lower lip, chin, and, sometimes, the paranasal regions. Due to the variability in the study designs and analysis methods, a direct comparison was not possible. Therefore, based on the results of this SR, guidelines to systematize the workflow for evaluating the accuracy of 3D soft tissue simulations in orthognathic surgery in future studies are proposed.

Correspondence attention for facial appearance simulation

In orthognathic surgical planning for patients with jaw deformities, it is crucial to accurately simulate the changes in facial appearance that follow the bony movement. Compared with the traditional biomechanics-based methods like the finite-element method (FEM), which are both labor-intensive and computationally inefficient, deep learning-based methods offer an efficient and robust modeling alternative. However, current methods do not account for the physical relationship between facial soft tissue and bony structure, causing them to fall short in accuracy compared to FEM. In this work, we propose an Attentive Correspondence assisted Movement Transformation network (ACMT-Net) to predict facial changes by correlating facial soft tissue changes with bony movement through a point-to-point attentive correspondence matrix. To ensure efficient training, we also introduce a contrastive loss for self-supervised pre-training of the ACMT-Net with a k-Nearest Neighbors (k-NN) based clustering. Experimental results on patients with jaw deformities show that our proposed solution can achieve significantly improved computational efficiency over the state-of-the-art FEM-based method with comparable facial change prediction accuracy.

Soft tissue prediction in orthognathic surgery: Improving accuracy by means of anatomical details

Three-dimensional virtual simulation of orthognathic surgery is now a well-established method in maxillo-facial surgery. The commercial software packages are still burdened by a consistent imprecision on soft tissue predictions. In this study, the authors produced an anatomically detailed patient specific numerical model for simulation of soft tissue changes in orthognathic surgery. Eight patients were prospectively enrolled. Each patient underwent CBCT and planar x-rays prior to surgery and in addition received an MRI scan. Postoperative soft-tissue change was simulated using Finite Element Modeling (FEM) relying on a patient-specific 3D models generated combining data from preoperative CBCT (hard tissue) scans and MRI scans (muscles and skin). An initial simulation was performed assuming that all the muscles and the other soft tissue had the same material properties (Homogeneous Model). This model was compared with the postoperative CBCT 3D simulation for validation purpose. Design of experiments (DoE) was used to assess the effect of the presence of the muscles considered and of their variation in stiffness. The effect of single muscles was evaluated in specific areas of the midface. The quantitative distance error between the homogeneous model and actual patient surfaces for the midface area was 0.55 mm, standard deviation 2.9 mm. In our experience, including muscles in the numerical simulation of orthognathic surgery, brought an improvement in the quality of the simulation obtained.

Comparison of regional soft tissue changes after bimaxillary rotational surgery between class III deformity with overbite and open bite: A 3D imaging analysis

BACKGROUND

This prospective study aimed to compare regional soft tissue changes between patients with class III overbite and open bite deformities treated with bimaxillary surgery involving clockwise and counter-clockwise mandibular setback, respectively.

MATERIAL AND METHODS

Class III deformity adults receiving Le Fort I and bilateral sagittal split osteotomies were grouped according to the incisal occlusion: overbite (n = 30) and open bite (n = 30). Combined cone-beam CT scans and 3D facial photographs preoperative and at least 1-year postoperative were taken to assess the soft tissue changes.

RESULTS

Postoperative changes for the overbite and open bite groups included anterior repositioning of nose (-0.8 ± 1.2 mm and -1.1 ± 1.1 mm, respectively) and cheek (-1.9 ± 1.3 mm and -1.7 ± 2.6 mm, respectively), posterior repositioning of chin (5.2 ± 4.0 mm and 4.9 ± 3.2 mm, respectively), and medial (-1.7 ± 2.0 mm and -1.9 ± 2.1 mm, respectively) and posterior (2.7 ± 1.4 mm and 2.8 ± 2.3 mm, respectively) repositioning of bilateral angles. Posterior (1.2 ± 2.0 mm and 5.1 ± 3.3 mm) and inferior (-1.4 ± 2.2 mm and -2.4 ± 2.7 mm) repositioning of upper lip and lower lip occurred in overbite group. Inferior (-2.3 ± 2.4 mm) and superior (3.7 ± 3.4 mm) repositioning of chin occurred in the overbite and open bite groups, respectively.

CONCLUSIONS

Treatment of class III overbite and open bite deformities with bimaxillary rotational surgery resulted in comparable regional soft tissue changes, except for upper lip, lower lip and chin.

Simulation of Postoperative Facial Appearances via Geometric Deep Learning for Efficient Orthognathic Surgical Planning

Orthognathic surgery corrects jaw deformities to improve aesthetics and functions. Due to the complexity of the craniomaxillofacial (CMF) anatomy, orthognathic surgery requires precise surgical planning, which involves predicting postoperative changes in facial appearance. To this end, most conventional methods involve simulation with biomechanical modeling methods, which are labor intensive and computationally expensive. Here we introduce a learning-based framework to speed up the simulation of postoperative facial appearances. Specifically, we introduce a facial shape change prediction network (FSC-Net) to learn the nonlinear mapping from bony shape changes to facial shape changes. FSC-Net is a point transform network weakly-supervised by paired preoperative and postoperative data without point-wise correspondence. In FSC-Net, a distance-guided shape loss places more emphasis on the jaw region. A local point constraint loss restricts point displacements to preserve the topology and smoothness of the surface mesh after point transformation. Evaluation results indicate that FSC-Net achieves 15× speedup with accuracy comparable to a state-of-the-art (SOTA) finite-element modeling (FEM) method.

Evaluation of soft tissue prediction accuracy for orthognathic surgery with skeletal class III malocclusion using maxillofacial regional aesthetic units

OBJECTIVES

This study aimed to evaluate the soft tissue prediction accuracy of patients undergoing orthognathic surgery to correct skeletal class III malocclusion using maxillofacial regional aesthetic units.

MATERIALS AND METHODS

Pre- and postoperative cone-beam computed tomography (CBCT) and 3D facial scans were taken for 58 patients who had undergone orthognathic surgery. The preoperative 3D facial scan was integrated with the preoperative CBCT using ProPlan CMF software. The software simulated the surgery and generated postoperative soft tissue prediction. The simulated 3D facial scan was then compared with the actual 3D facial scan obtained at least 6 months after the surgery by the maxillofacial regional aesthetic units and the facial soft tissue landmark points.

RESULTS

The anatomical regions of the upper lip, lower lip, chin, right external buccal and left external buccal prediction were above 2.0 mm. As for the soft tissue landmarks, at chl, chr, ls, stm and li, the position of predicted scan was higher than that of the actual postoperative scan.

CONCLUSIONS

The accuracy of 3D soft tissue predictions using ProPlan CMF software in Skeletal III patients was clinically satisfactory according to maxillofacial regional aesthetic units combined with facial soft tissue landmark points. However, the accuracy of prediction still needed improvement in some areas.

CLINICAL RELEVANCE

The accuracy of soft tissue prediction can be analyzed more clearly through maxillofacial regional aesthetic units so that clinicians have a deeper understanding of the use of the software to predict soft tissue change after orthognathic surgery.

A Biomechanical Analysis of the Influence of the Morfology of the Bone Blocks Grafts on the Transfer of Tension or Load to the Soft Tissue by Means of the Finite Elements Method

Edentulism produces resorption of alveolar bone processes, which can complicate placement of dental implants. Guided bone regeneration techniques aim to recover the volume of bone. These treatments are susceptible to the surgical technique employed, the design of the autologous block or the tension of the suture. These factors can relate to major complications as the lack of primary closure and dehiscence. The present study, using finite element analysis, aimed to determine differences in terms of displacement of the oral mucosa, transferred stress according to Von Mises and deformation of soft tissue when two block graft designs (right-angled and rounded) and two levels of suture tension (0.05 and 0.2 N) were combined. The results showed that all the variables analyzed were greater with 0.2 N. Regarding the design of the block, no difference was found in the transferred stress and deformation of the soft tissue. However, displacement was related to a tendency to dehiscence (25% greater in the right-angled/chamfer design). In conclusion different biomechanical behavior was observed in the block graft depending on the design and suture tension, so it is recommended to use low suture tension and rounded design. A novel finite element analysis model is presented for future investigations.

A Quantitative and Qualitative Clinical Validation of Soft Tissue Simulation for Orthognathic Surgery Planning

The purpose of this study was to perform a quantitative and qualitative validation of a soft tissue simulation pipeline for orthognathic surgery planning, necessary for clinical use. Simulation results were retrospectively obtained in 10 patients who underwent orthognathic surgery. Quantitatively, error was measured at 9 anatomical landmarks for each patient and different types of comparative analysis were performed considering two mesh resolutions, clinically accepted error, simulation time and error measured by means of percentage of the whole surface. Qualitatively, evaluation and binary questions were asked to two surgeons, both before and after seeing the actual surgical outcome, and their answers were compared. Finally, the quantitative and qualitative results were compared to check if these two types of validation are correlated. The quantitative results were accurate, with greater errors corresponding to gonions and lower lip. Qualitatively, surgeons answered similarly mostly and their evaluations improved when seeing the actual outcome of the surgery. The quantitative validation was not correlated to the qualitative validation. In this study, quantitative and qualitative validations were performed and compared, and the need to carry out both types of analysis in validation studies of soft tissue simulation software for orthognathic surgery planning was proved.

Deep learning for biomechanical modeling of facial tissue deformation in orthognathic surgical planning

PURPOSE

Orthognathic surgery requires an accurate surgical plan of how bony segments are moved and how the face passively responds to the bony movement. Currently, finite element method (FEM) is the standard for predicting facial deformation. Deep learning models have recently been used to approximate FEM because of their faster simulation speed. However, current solutions are not compatible with detailed facial meshes and often do not explicitly provide the network with known boundary type information. Therefore, the purpose of this proof-of-concept study is to develop a biomechanics-informed deep neural network that accepts point cloud data and explicit boundary types as inputs to the network for fast prediction of soft-tissue deformation.

METHODS

A deep learning network was developed based on the PointNet++ architecture. The network accepts the starting facial mesh, input displacement, and explicit boundary type information and predicts the final facial mesh deformation.

RESULTS

We trained and tested our deep learning model on datasets created from FEM simulations of facial meshes. Our model achieved a mean error between 0.159 and 0.642 mm on five subjects. Including explicit boundary types had mixed results, improving performance in simulations with large deformations but decreasing performance in simulations with small deformations. These results suggest that including explicit boundary types may not be necessary to improve network performance.

CONCLUSION

Our deep learning method can approximate FEM for facial change prediction in orthognathic surgical planning by accepting geometrically detailed meshes and explicit boundary types while significantly reducing simulation time.

Virtual Surgical Planning: Modeling from the Present to the Future

Virtual surgery planning is a non-invasive procedure, which uses digital clinical data for diagnostic, procedure selection and treatment planning purposes, including the forecast of potential outcomes. The technique begins with 3D data acquisition, using various methods, which may or may not utilize ionizing radiation, such as 3D stereophotogrammetry, 3D cone-beam CT scans, etc. Regardless of the imaging technique selected, landmark selection, whether it is manual or automated, is the key to transforming clinical data into objects that can be interrogated in virtual space. As a prerequisite, the data require alignment and correspondence such that pre- and post-operative configurations can be compared in real and statistical shape space. In addition, these data permit predictive modeling, using either model-based, data-based or hybrid modeling. These approaches provide perspectives for the development of customized surgical procedures and medical devices with accuracy, precision and intelligence. Therefore, this review briefly summarizes the current state of virtual surgery planning.

Soft-Tissue Simulation for Computational Planning of Orthognathic Surgery

Simulation technologies offer interesting opportunities for computer planning of orthognathic surgery. However, the methods used to date require tedious set up of simulation meshes based on patient imaging data, and they rely on complex simulation models that require long computations. In this work, we propose a modeling and simulation methodology that addresses model set up and runtime simulation in a holistic manner. We pay special attention to modeling the coupling of rigid-bone and soft-tissue components of the facial model, such that the resulting model is computationally simple yet accurate. The proposed simulation methodology has been evaluated on a cohort of 10 patients of orthognathic surgery, comparing quantitatively simulation results to post-operative scans. The results suggest that the proposed simulation methods admit the use of coarse simulation meshes, with planning computation times of less than 10 seconds in most cases, and with clinically viable accuracy.

A novel incremental simulation of facial changes following orthognathic surgery using FEM with realistic lip sliding effect

Accurate prediction of facial soft-tissue changes following orthognathic surgery is crucial for surgical outcome improvement. We developed a novel incremental simulation approach using finite element method (FEM) with a realistic lip sliding effect to improve the prediction accuracy in the lip region. First, a lip-detailed mesh is generated based on accurately digitized lip surface points. Second, an improved facial soft-tissue change simulation method is developed by applying a lip sliding effect along with the mucosa sliding effect. Finally, the orthognathic surgery initiated soft-tissue change is simulated incrementally to facilitate a natural transition of the facial change and improve the effectiveness of the sliding effects. Our method was quantitatively validated using 35 retrospective clinical data sets by comparing it to the traditional FEM simulation method and the FEM simulation method with mucosa sliding effect only. The surface deviation error of our method showed significant improvement in the upper and lower lips over the other two prior methods. In addition, the evaluation results using our lip-shape analysis, which reflects clinician's qualitative evaluation, also proved significant improvement of the lip prediction accuracy of our method for the lower lip and both upper and lower lips as a whole compared to the other two methods. In conclusion, the prediction accuracy in the clinically critical region, i.e., the lips, significantly improved after applying incremental simulation with realistic lip sliding effect compared with the FEM simulation methods without the lip sliding effect.

Three-dimensional virtual planning in mandibular advancement surgery: Soft tissue prediction based on deep learning

The study aimed at developing a deep-learning (DL)-based algorithm to predict the virtual soft tissue profile after mandibular advancement surgery, and to compare its accuracy with the mass tensor model (MTM). Subjects who underwent mandibular advancement surgery were enrolled and divided into a training group and a test group. The DL model was trained using 3D photographs and CBCT data based on surgically achieved mandibular displacements (training group). Soft tissue simulations generated by DL and MTM based on the actual surgical jaw movements (test group) were compared with soft-tissue profiles on postoperative 3D photographs using distance mapping in terms of mean absolute error in the lower face, lower lip, and chin regions. 133 subjects were included - 119 in the training group and 14 in the test group. The mean absolute error for DL-based simulations of the lower face region was 1.0 ± 0.6 mm and was significantly lower (p = 0.02) compared with MTM-based simulations (1.5 ± 0.5 mm). CONCLUSION: The DL-based algorithm can predict 3D soft tissue profiles following mandibular advancement surgery. With a clinically acceptable mean absolute error. Therefore, it seems to be a relevant option for soft tissue prediction in orthognathic surgery. Therefore, it seems to be a relevant options.

Application of Virtual Planning for Three-Dimensional Guided Maxillofacial Reconstruction of Pruzansky-Kaban III Hemifacial Microsomia Using Custom Made Fixation Plate

PURPOSE

Pruzansky-Kaban III hemifacial microsomia (HFM) is a rare congenital facial deformity, and it is challenging to reconstruct the facial appearance. The aim of the present study was to describe a technique of application of virtual planning for three-dimensional (3D) guided maxillofacial reconstruction of Pruzansky-Kaban III HFM using custom made fixation plate.

METHODS

With the help of 3D models, a preoperative virtual planning and surgical simulation were performed. Computer-aided design/computer-aided manufacture (CAD/CAM) patient customized guides and custom fixation plates were designed to reconstruct the maxillofacial skull intraoperatively. Assessment was achieved through evaluation of the postoperative effects, such as imaging, facial appearance recovery and operation time.

RESULTS

Five patients with Pruzansky-Kaban III HFM were enrolled into this study. The results showed an exceptional accuracy between the preoperative virtual planning and the outcomes actually achieved postoperatively. Intraoperative measurements were no longer needed and the different surgical steps became more simple and easier. The total time was distributed as: 160 minutes for the surgical time, 40 minutes for preoperative virtual plan, and 80 minutes for designing the patient specific cutting guides and custom fixation plates. The operating time and tissue damage were reduced. All cases underwent uneventful healing without any complications.

CONCLUSION

The technique of patient specific guides and custom fixation plates is a reliable method of conveying the virtual plan to the operative field with higher efficiency in patients with Pruzansky-Kaban III HFM.

Accuracy of three-dimensional virtual simulation of the soft tissues of the face in OrtogOnBlender for correction of class II dentofacial deformities: an uncontrolled experimental case-series study

Purpose

To assess whether virtual simulations of the projection of the soft tissues of the face after class II bimaxillary orthognathic surgery, generated from 3D reconstruction of preoperative computed tomography (CT) scans, differed significantly from the actual soft tissue profile obtained in the late postoperative period (beyond 6 months). Secondarily, to validate the accuracy of a free, open-source software suite for virtual soft tissue planning in orthognathic surgery.

Methods

Helical CT scans were obtained pre- and postoperatively from 16 patients with Angle class II malocclusion who underwent bimaxillary orthognathic surgery. A comparative study between soft tissue meshes constructed for surgical simulation (M1) and the actual meshes obtained from postoperative scans (M2) was then performed. To establish the accuracy of 3D facial soft tissue simulation in a free and open-source software suite (OrtogOnBlender-OOB), 17 predetermined anatomic landmarks were measured in M1 and M2 scans after alignment of cranial structures.

Results

The mean error between preoperative simulations and actual postoperative findings was < 2 mm for all anthropometric landmarks. The overall average error for the facial soft tissues was 1.07 mm.

Conclusion

Comparison between preoperative simulation (M1) and actual postoperative findings (M2) showed clinically relevant ability of the method to reproduce actual surgical movement reliably (< 2-mm error). OOB is capable of accurate soft tissue planning for orthognathic surgery, but mesh deformation methods still require improvement.

Trial registration

RBR-88jff9. Retrospectively registered at Brazilian Registry of Clinical trials-ReBec (http://www.ensaiosclinicos.gov.br) May 06, 2020.

Orthodontic-Orthognathic Management of a patient with skeletal class II with bimaxillary protrusion, complicated by vertical maxillary excess: A multi-faceted case report of difficult treatment management issues

This case reports the unsuccessful first treatment and the subsequent retreatment of a 35-year old Asian female with a skeletal class II with bimaxillary protrusion, complicated by a deep bite and vertical maxillary excess. This case report highlights the multiple facets of a challenging treatment plan and discusses the ramifications of treatment when treatment does not go as planned. The initial treatment plan consisted of a surgical approach with a maxillary Le Fort I surgery to correct the malocclusion as per the patient's requests without mandibular surgery due to the inherent risk of paraesthesia. The second treatment plan consisted of a bimaxillary surgery with genioplasty. The surgical treatment utilized virtual surgical planning (VSP). The orthodontic treatment was concluded with a corrected overjet and overbite achieving optimum function and balancing the facial profile aesthetically. This case report highlights the need for clear communication of the treatment plan and also the unpredictability of certain treatment outcomes especially when the literature does not provide for definitive conclusions. In addition, it sheds light on the challenge of unpredictable response of soft tissue after surgical treatment and the importance of patient expectations of outcomes. It is hoped that the paper provides a platform for future discussions of difficult malocclusions.

3D Soft-Tissue Prediction Methodologies for Orthognathic Surgery—A Literature Review

Three-dimensional technologies have had a wide diffusion in several fields of application throughout the last decades; medicine is no exception and the interest in their introduction in clinical applications has grown with the refinement of such technologies. We focus on the application of 3D methodologies in maxillofacial surgery, where they can give concrete support in surgical planning and in the prediction of involuntary facial soft-tissue changes after planned bony repositioning. The purpose of this literature review is to offer a panorama of the existing prediction methods and software with a comparison of their reliability and to propose a series of still pending issues. Various software are available for surgical planning and for the prediction of tissue displacements, but their reliability is still an unknown variable in respect of the accuracy needed by surgeons. Maxilim, Dolphin and other common planning software provide a realistic result, but with some inaccuracies in specific areas of the face; it also is not totally clear how the prediction is obtained by the software and what is the theoretical model they are based on.

A New Approach of Predicting Facial Changes following Orthognathic Surgery using Realistic Lip Sliding Effect

Accurate prediction of facial soft-tissue changes following orthognathic surgery is crucial for improving surgical outcome. However, the accuracy of current prediction methods still requires further improvement in clinically critical regions, especially the lips. We develop a novel incremental simulation approach using finite element method (FEM) with realistic lip sliding effect to improve the prediction accuracy in the area around the lips. First, lip-detailed patient-specific FE mesh is generated based on accurately digitized lip surface landmarks. Second, an improved facial soft-tissue change simulation method is developed by applying a lip sliding effect in addition to the mucosa sliding effect. The soft-tissue change is then simulated incrementally to facilitate a natural transition of the facial change and improve the effectiveness of the sliding effects. A preliminary evaluation of prediction accuracy was conducted using retrospective clinical data. The results showed that there was a significant prediction accuracy improvement in the lip region when the realistic lip sliding effect was applied along with the mucosa sliding effect.

Facial morphology prediction after complete denture restoration based on principal component analysis

In developing treatment plan before complete denture restoration, doctors need to help the patient regain chewing ability while considering facial shape reconstruction after the surgery. At present, facial deformation prediction depends on the subjective judgment and experience of doctors; thus, an accurate basis for scientific quantitative analysis is lacking. With the development of computer technology, this paper proposed new facial morphology prediction method based on principal component analysis. Firstly, the curvature feature template with few feature points is constructed to replace the deformed areas of facial models. Secondly, the principal component analysis method is used to construct an elastic deformation prediction model for complex skin tissue. Finally, the Laplacian deformation technology is used to reconstruct the facial model and to obtain an intuitive digital 3D model. This method can adjust the facial deformation amplitude interactively by controlling shape parameters and predict the effect in consideration of different doctors' varied needs and habits. The experiments show that this method can predict the facial models interactively and the average deviation between the prediction models and the post-treatment facial models is between -2.102 and 2.102 mm by adjusting the shape parameters.

A reversed approach for simultaneous mandibular symphyseal split osteotomy and genioplasty

Performing a mandibular symphyseal split and genioplasty simultaneously and accurately is a technical challenge for the surgeon. The aim of this study was to validate a reversed approach for simultaneous symphyseal split and genioplasty. A cutting guide and a repositioning guide were designed and printed three-dimensionally in titanium. The symphyseal split and genioplasty were performed successfully. The accuracy of the technique appears to be appropriate for clinical application.

Three-dimensional region-based study on the relationship between soft and hard tissue changes after orthognathic surgery in patients with prognathism

Both deep understanding and reliable prediction of postoperative soft tissue changes are crucial for planning orthognathic surgery. Instead of estimating soft tissue responses by measuring individual landmark changes, this study aimed to investigate the relationship (ratio) between soft and hard tissue movements in different facial regions through three-dimensional cone-beam computed tomography (CBCT). Preoperative and postoperative CBCT images were superimposed using the surface registration method on the basis of the cranial base, and 10 facial regions of interest were defined. Region-based volumetric subtractions between the preoperative and postoperative segments were performed. The volumetric differences and surface of each region were used to estimate the average movement. Correlation and regression analyses were performed to examine the relationships between the corresponding soft and hard tissue movements. An overall pattern of facial soft tissue movement was observed in patients with prognathism who underwent orthognathic surgery. The experiment results have shown that mean ratios for the average soft-to-hard tissue movements in the facial regions varied, which may not exactly be similar to the published reports because of the population biocharacteristics and study methods, but the trend is in agreement with the previous studies. Additionally, the prediction capability of the regression model was significantly high, ranging from 0.786 to 0.857, in upper lip, upper vermilion, and chin regions, thus demonstrating that the skin outline changes in these critical regions could be reliably predicted from the underlying bone movements. These results could likely be applied in future soft tissue simulation in orthognathic surgery.